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Abstract

Motivated by inspection of complex underwater environments, we have developed a
system for multi-sensor SLAM utilizing both structured and unstructured environmental
features. We present a system for deriving planar constraints from sonar data,
and jointly optimizing the vehicle and plane positions as nodes in a factor graph. We
also present a system for outlier rejection and smoothing of 3D sonar data, and for
generating loop closure constraints based on the alignment of smoothed submaps.
Our factor graph SLAM backend combines loop closure constraints from sonar data
with detections of visual fiducial markers from camera imagery, and produces an online
estimate of the full vehicle trajectory and landmark positions. We evaluate our
technique on an inspection of a decomissioned aircraft carrier, as well as synthetic
data and controlled indoor experiments, demonstrating improved trajectory estimates
and reduced reprojection error in the final 3D map.

Description

Submitted in partial fulfillment of the requirements for the degree of Master of Science at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution February 2014

A novel distributed underwater acoustic networking (UAN) protocol suitable for ad-hoc
deployments of both stationary and mobile nodes dispersed across a relatively
wide coverage area is presented. Nodes are dynamically ...

The spatial and temporal evolution of stratified shear instabilities is quantified in a highly stratified and energetic estuary. The measurements are made using high-resolution acoustic backscatter from an array composed ...

Underwater acoustic communication is an extremely complex field that faces many
challenges due to the time-varying nature of the ocean environment. Vector sensors
are a proven technology that when utilizing their directional ...

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